Image analysis systems can be used to inspect the condition, health, or status of various target objects, such as machinery in industrial, automotive, and aeronautic applications. The image analysis systems typically inspect a given target object by obtaining image data of the target object in operation. The image analysis systems analyze the image data that is received using one or more neural networks and/or other image analysis techniques to search for specific features or objects within the image data. The automated analysis provided by the image analysis systems may be significantly more efficient and/or accurate than traditional manual inspection performed by a human operator either examining the target object directly or examining the image data of the target object.
The analysis techniques and calculations employed by the automated image analysis systems may be computationally intensive. Due to the complex computations, the efficiency of known image analysis systems may be reduced as the workload of image data to analyze increases. The inefficiency due to the increased workload may slow down the speed at which the image analysis systems are able to analyze the image data and reach an outcome (e.g., relative to more efficient image analysis processes and systems). The inefficiency may also require addition electrical energy for powering and/or cooling the processing hardware that performs the computations. One option for improving the operational efficiency of the image analysis systems is to modify the image analysis techniques to reduce the complexity and intensity of the computations performed. However, simplifying the image analysis techniques may denigrate the quality (e.g., accuracy and/or precision) of the inspection.